The Yield Prediction of Synthetic Fuel Production from Pyrolysis of Plastic Waste by Levenberg–Marquardt Approach in Feedforward Neural Networks Model
Document Type
Article
Publication Date
1-1-2019
Abstract
The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature. © 2019 by the authors.
Keywords
plastic waste, pyrolysis, artificial neural network, prediction, fuel
Divisions
fac_eng
Funders
Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (DF-435-829-1441)
Publication Title
Polymers
Volume
11
Issue
11
Publisher
MDPI